Searching an image in an image database is usually carried out by two types of queries. The first type of query searches images based on a “search word” in a search request; i.e., searching for images whose filename in an image contains the “search word”. The second type of query searches for images based on the similarity of content of the images in an image database and the image included in a search request. The searching technology used in this application refers to the second type of query.
The conventional image searching process is illustrated in FIG. 1. At 110, a search engine receives a search request which includes a requested image. At 120, the search engine calculates features of the requested image to form a search request. For example, the search engine may extract the shape features of the requested image. At 130, the search engine searches for images most similar to the extracted features in the image database and returns a search result. The features of all the images in the image database may be pre-calculated.
To provide the most up-to-date search result, the images in the image database may be updated periodically, and new images may be added to the database.
Conventionally, both existing images and newly added images in the image database are updated together; the index of those images is also updated. The update process is usually performed daily during the idlest period of a search engine. During this period, millions of existing and newly added images may be processed together. Since the number of existing images is often far more than the newly added images, updating the existing images and adding new images together may require excessively processing time. Therefore, a more efficient method and system for updating images in an image database is needed.